The present invention relates to x-ray imaging, and more particularly, to automatic left atrium segmentation within medical images of the heart.
According to the American Heart Association, 15% of all strokes (the second ranking cause of death worldwide) are caused by atrial fibrillation (AF). AF is a disease related to the left atrium (LA) which is responsible for pumping oxygenated blood to the left ventricle. During AF, the LA quivers in an abnormal rhythm which results in blood failing to pump out effectively. As a result, the blood residing in the LA is likely to form clots. The clots block the passage of blood flowing into smaller blood vessels, which causes strokes.
Radio-frequency catheter ablation is a widely used minimally invasive approach to treat AF. This particular ablation procedure uses high radio-frequency energy to eliminate sources of ectopic foci, which are abnormal pacemaker sites within the heart. Segmentation of the LA is extremely important in pre-operative assessments to identify potential sources of abnormal electrical events related to ectopic foci. Recent research has found that pulmonary venous drainages are also a primary cause of ectopic foci. Thus, it is important that the LA chamber body and pulmonary venous drainages of patients be detected accurately to facilitate a proper ablation strategy for treating AF.
Large variations in drainage patterns exist within any given population. Approximately 71% of the world population possesses two ostia on the right side of the heart allowing blood passage for the upper and lower lobe veins. The remaining approximately 29% of the world population possess anywhere from three to five ostia on the right side of the heart. In rare cases, a person may only have a single right venous ostium.
Variations in the left side of the heart are smaller, since approximately 86% of the population has two separate ostia for the upper and lower lobe veins. Identification of the drainage pattern and extraction of a personalized LA model would be helpful in creating user-defined ablation strategies (e.g., circumferential segmented ablation) tailored for specific patient anatomies.
Accordingly, an automated method for extraction of a personalized LA model and pulmonary vein model for supporting personalized ablation strategies is desirable.